Update summarizer.py

summarizer.py

@@ -1,36 +1,36 @@
-import os
-import base64
-from langchain.docstore.document import Document
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.llms.openai import OpenAI
-from langchain.chains.summarize import load_summarize_chain
-from langchain.document_loaders import UnstructuredURLLoader
-import nltk
-import openai
-
-nltk.download('punkt')
-OPENAI_API_KEY = "sk-proj-uCiflA45fuchFdjkbNJ7T3BlbkFJF5WiEf2zHkttr7s9kijX"
-
-
-def create_brand_html(brand_link):
-    urls = [brand_link]
-    loader = UnstructuredURLLoader(urls=urls)
-    data = loader.load()
-    chunk_size = 3000
-    chunk_overlap = 200
-    text_splitter = CharacterTextSplitter(
-        chunk_size=chunk_size,
-        chunk_overlap=chunk_overlap,
-        length_function=len,
-    )
-    texts = text_splitter.split_text(data[0].page_content)
-    docs = [Document(page_content=t) for t in texts[:]]
-    return docs
-
-
-def create_langchain_openai_query(docs):
-    openai.api_key = OPENAI_API_KEY
-    llm = OpenAI(temperature=0, openai_api_key=openai.api_key)
-    map_reduce_chain = load_summarize_chain(llm, chain_type="map_reduce")
-    output = map_reduce_chain.run(docs)
-    return output
+import os
+import base64
+from langchain.docstore.document import Document
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.llms.openai import OpenAI
+from langchain.chains.summarize import load_summarize_chain
+from langchain.document_loaders import UnstructuredURLLoader
+import nltk
+import openai
+
+nltk.download('punkt')
+OPENAI_API_KEY = ""
+
+
+def create_brand_html(brand_link):
+    urls = [brand_link]
+    loader = UnstructuredURLLoader(urls=urls)
+    data = loader.load()
+    chunk_size = 3000
+    chunk_overlap = 200
+    text_splitter = CharacterTextSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        length_function=len,
+    )
+    texts = text_splitter.split_text(data[0].page_content)
+    docs = [Document(page_content=t) for t in texts[:]]
+    return docs
+
+
+def create_langchain_openai_query(docs):
+    openai.api_key = OPENAI_API_KEY
+    llm = OpenAI(temperature=0, openai_api_key=openai.api_key)
+    map_reduce_chain = load_summarize_chain(llm, chain_type="map_reduce")
+    output = map_reduce_chain.run(docs)
+    return output